Direct marketing approaches are constantly evolving and becoming more complex. In addition to traditional methods of making an offer through direct mail or telemarketing, channels such as email or online offers through a website have increased the number of campaigns that the marketer may consider. Furthermore, advances in analytical software in recent years have provided marketers with better predictive models of their customer behavior, and these models often have a very high degree of sophistication. For example, it is not uncommon to have separate models of response probability for an offer, such as a credit card offer, through the call center, direct mail, and email. The marketer would like to use this information to determine the best course of action when deciding which customers should receive each offer through each channel. An objective is to maximize or minimize some quantitative measure of the offers that are made, such as maximizing the expected response probability or the expected profit, or minimizing total cost. As a further complication to the problem, marketing actions are limited by business constraints that are to be satisfied. These constraints could be divided into categories such as: aggregate constraints and contact policy (“individual”) constraints.
Aggregate constraints involve a limit that is applied over a large number of customers, whether it is the entire customer population or a subset of the customers. For example, constraints on budgets, channel usage, and the number of offers made are types of aggregate constraints, as are constraints on measures such as overall average return, behavior, or risk. An aggregate constraint does not apply to the offer decisions associated with an individual customer but rather concerns the overall impact of making offers to a large group of customers.
On the other hand, contact policy constraints impose restrictions on the combinations of offers that can be made to individual customers. Thus, unlike aggregate constraints, each contact policy constraint involves only the offer decisions associated with a single customer. For example, a contact policy constraint might state that a customer can receive no more than two credit card offers every six weeks, or it might specify that if a customer receives an email offer, then he cannot receive an offer through the call center for at least two weeks.
FIG. 1 is a block diagram depicting a process 30 for planning a marketing campaign. In planning a campaign, a campaign manager defines the offers and the mechanisms for delivering offers for the campaign as shown at 32. Prior to campaign execution 40, a statistical modeler develops statistical models 34 predicting the effectiveness of different offer/delivery mechanism combinations. A marketing analyst receives the campaign definition 32 as well as the statistical models 34 and attempts to optimize the campaign strategy 36. For example, the marketing analyst may seek to maximize predicted revenues from the selected campaigns within a given marketing budget. The marketing analyst's optimization process may be an iterative one where the analyst examines the optimization reports 38 and repeats the optimization procedures 36 if he believes that the campaign strategy can be bettered (e.g., through modification of the objective, constraints, or contact policies). Once a campaign strategy is decided upon, a campaign manager executes the selected strategy 40.
FIG. 2 is a depiction of an example marketing campaign 50. In this campaign 50, a marketing strategy is sought that provides selected offers from a plurality of offers 52 to a plurality of customers 54. In the example of FIG. 2, the marketing analyst selects among three offers (i.e., providing a Visa Classic card, a Visa Gold card, or a Home Equity Loan) and three contact methods (i.e., providing the offer by direct mail, over the phone through a call center, or in person at a branch office) resulting in nine possible campaign alternatives 52 for each of the plurality of customers 54. Each of the customer-campaign alternative combinations has a value (55, 56, 57) associated with the combination. In the example of FIG. 2, this value (55, 56, 57) is an expected return from the customer-campaign alternative combination (i.e., a call center offer of a Visa Classic card to customer #1 has an expected return of $4.90 as shown at 55). The expected return value may be calculated through evaluation of an objective function.
FIG. 3 is a further depiction of the example marketing campaign illustrating a large number of customer-campaign alternative combinations 60. The example of FIGS. 2 and 3, having nine customers and nine campaign alternatives, results in 81 possible customer-campaign alternative combinations if each customer is to receive one campaign offer 52. One can see from this simplified example that a more realistic marketing campaign having potentially millions of customers and twenty or thirty candidate offers 52 becomes extremely complex and difficult to manage. For example, a system having three million customers and twenty-five campaign options would result in seventy-five million customer-campaign alternative combinations.
The complexity of the marketing campaign is further exacerbated by the introduction of constraints. As described above, many different constraints may be involved, such as aggregate constraints involving a limit placed over the global set of customers, individual constraints that dictate rules for each individual customer, etc. FIG. 4 is a diagram illustrating a visualization 70 of a marketing optimization problem. The objective function 72 to be maximized or minimized and the aggregate constraints 74 are represented on one row each as they are applicable to all of the customers. Each customer's contact policy constraints are then assigned a block 76, 78, 80.
One can quickly see how the structure of FIG. 4 becomes large in a real-world marketing problem having millions of customers. For example, the system described above, having three million customers, would result in a visualization structure having three million sets of blocks. Combined with seventy-five million customer-campaign alternative combinations, marketing campaigns on this scale become difficult to manage and timely solve for an optimum solution.